Search Results (464)

Triboelectric nanogenerators (TENG) are mechanical energy harvesters characterized by high sensitivity and simple structure and are currently being widely developed for use in human body motion sensing. Among them, fiber-based TENGs (FTENG) are particularly suitable for wearable human motion sensors due to their unique structure, which offers flexibility, high durability, and comfort. However, studies involving doping to further modify the electrical output characteristics of FTENGs are very limited. Here, we propose an innovative approach that combines graphite (GP) doping with fiber-based TENG fabrication, successfully developing a graphite-doped polyester fiber-based TENG (GP@PET-TENG). Proper graphite doping can increase the amount of transferred charge and thus improve the output electrical performance of TENG, but this method has rarely been explored in FTENG. With the incorporation of 3%wt graphite, the open-circuit voltage of the GP@PET-TENG increased from 103.3 V to 202.1 V, and the short-circuit current increased from 60.7 μA to 105.1 μA, compared to the pure polyester fiber based TENG (PET-TENG). The device achieved a maximum output power of 4.15 mW (2.59 W/m²), demonstrates the capability to charge various capacitors, and successfully lit up 200 LEDs. By attaching the GP@PET tribo-layer to human skin, a single-electrode mode TENG can be formed, which captures the subject’s motion signals through skin contact and separation, converting them into voltage outputs. In fist-clenching and wrist-bending tests, motion-induced voltage signals up to 0.6 V were recorded, demonstrating the potential applications in rehabilitation assistance and mechanical control. Full article

In this paper, a method to improve the protection of a four-core power cable of a salt-producing floating platform equipped with an automatic breaker with an independent tripping mechanism is suggested. The use of this automatic breaker in combination with a suggested protection device ensures reliable protection of not only the power cables of the platform against all faults but also the personnel of the platform and animals on the reservoir banks against electric shock in the event of a single-phase ground fault in reservoir water. This would be possible due to a voltage sensor made in the form of a metal ring on the power cable and a relay; one terminal of the relay winding is connected to the voltage sensor by a single-core control cable, and the other to the neutral of a power source on the platform. The typically open contacts of this relay are connected to an electric circuit which includes a power source and a coil for an independent tripping mechanism of the automatic breaker. This design ensures reliable operation of the suggested protection device in the event of a single-phase ground fault in the power cable of the platform when underwater cable insulation is damaged. Full article

Plant microbial fuel cells (PMFCs) represent an eco-friendly solution for generating clean energy by converting biological processes into electricity. This work presents the first integration of tin sulfide (SnS)-coated 304 stainless steel (SS304) electrodes into Aloe vera-based PMFCs for enhanced energy harvesting. SnS thin films were obtained via chemical bath deposition and screen printing, followed by thermal treatment. X-ray diffraction (XRD) revealed a crystal size of 15 nm, while scanning electron microscopy (SEM) confirmed film thicknesses ranging from 3 to 13.75 µm. Over a 17-week period, SnS-coated SS304 electrodes demonstrated stable performance, with open circuit voltages of 0.6–0.7 V and current densities between 30 and 92 mA/m², significantly improving power generation compared to uncoated electrodes. Polarization analysis indicated an internal resistance of 150 Ω and a power output of 5.8 mW/m². Notably, the system successfully charged a 15 F supercapacitor with 8.8 J of stored energy, demonstrating a practical proof-of-concept for powering low-power IoT devices and advancing PMFC applications beyond power generation. Microbial biofilm formation, observed via SEM, contributed to enhanced electron transfer and system stability. These findings highlight the potential of PMFCs as a scalable, cost-effective, and sustainable energy solution suitable for industrial and commercial applications, contributing to the transition toward greener energy systems. These incremental advances demonstrate the potential of combining low-cost electrode materials and energy storage systems for future scalable and sustainable bioenergy solutions. Full article

To establish a reliable thermoelectric module evaluation, a Standard Reference Thermoelectric Module (SRTEM) was developed based on stability. Open-circuit voltage (V_oc) was selected as the key calibration parameter due to its consistent response to temperature differences (ΔT). The SRTEM consists of eight p–n thermoelectric couples composed of metallic thermoelectric materials—Ni₉₀Cr₁₀ (chromel), Cu₅₅Ni₄₅ (constantan), Fe₆₄Ni₃₆ (invar), and pure Fe—selected based on their thermoelectric properties, structural compatibility, and contact resistance. Among the tested combinations, the chromel–constantan pair exhibited the highest V_oc of 55 mV at ΔT = 150 K. To increase V_oc and expand the usable calibration range, leg-shape modification and substrate replacement were investigated. Module simulation revealed that replacing the rectangular-leg geometry with a double-hourglass (2H/G) structure could increase V_oc by 20.2%. Furthermore, measurement of single-leg modules with substrates attached confirmed a 16.0% improvement in V_oc for the 2H/G shape over the rectangular shape, consistent with the predicted enhancement due to increased thermal resistance. In addition, replacing the alumina substrate with a higher thermal conductivity material, such as AlN, increased ΔT across the legs and yielded a further 9.1% improvement in V_oc. These results demonstrate the potential of the proposed SRTEM as a calibration standard for consistent thermoelectric module measurements. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries under complex operating conditions remains challenging, as the SOC signal combines a global linear (quasi-linear) trend with localized dynamic fluctuations driven by polarization, ion diffusion, temperature gradients, and load transients. In practice, open-circuit-voltage (OCV) approaches are affected by hysteresis and parameter drift, while high-fidelity electrochemical models require extensive parameterization and significant computational resources that hinder their real-time deployment in battery management systems (BMS). Purely data-driven methods capture temporal patterns but may under-represent abrupt local fluctuations and blur the distinction between trend and fluctuation, leading to biased SOC tracking when operating conditions change. To address these issues, LF-Net is proposed. The architecture decomposes battery time series into long-term trend and local fluctuation components. A linear branch models the quasi-linear SOC evolution. Multi-scale convolutional and differential branches enhance sensitivity to transient dynamics. An adaptive Fusion Module aggregates the representations, improving interpretability and stability, and keeps the parameter budget small for embedded hardware. Our experimental results demonstrate that the proposed model achieves a mean absolute error (MAE) of 0.0085 and a root-mean-square error (RMSE) of 0.0099 at 40 °C, surpassing mainstream models and confirming the method’s efficacy. Full article

The growing deployment of photovoltaic (PV) power plants has made reliable fault detection and classification a critical challenge for ensuring operational efficiency, safety, and economic viability. Faults on the direct current (DC) side, especially during the commissioning phase, can significantly affect power output and maintenance costs. This paper proposes a fault classification methodology for the direct current (DC) side of PV power plants, using the MATLAB/Simulink 2023b simulation environment for system modeling and dataset generation. The method accounts for different environmental and operational conditions—including irradiance and temperature variations—to enhance fault identification robustness. The main electrical faults—such as open circuit (OC), short circuit (SC), connector faults, and partial shading—are analyzed based on features extracted from current–voltage (I–V) and power–voltage (P–V) curves. The proposed classification system achieved 100% accuracy by applying the One-Versus-One (OVO) and One-Versus-Rest (OVR) techniques, using a dataset with 704 samples for one string and 2480 samples for three strings. The lowest accuracies were observed with the OVO technique: 99.03% for 1024 samples with one string, and 97.35% for 880 samples with three strings. The study also highlights the performance of multiclass machine learning techniques across different dataset sizes. The results reinforce the relevance of using machine learning integrated into the commissioning phase of PV systems, with the potential to improve reliability, reduce losses, and optimize the operational costs of solar plants. Future work should explore the application of this method to real-world data, as well as its deployment in the field to support companies and professionals in the sector. Full article

This work investigates the effect of interface traps on the impedance spectra of dopant-free silicon solar cells. The studied device consists of a crystalline silicon absorber with an a-Si:H/MoOx/ITO stack as the front passivating hole-collecting contact and an a-Si:H/LiF/Al stack as the rear passivating electron-collecting contact. Experimental measurements, including illuminated current–voltage (I–V) characteristics and impedance spectroscopy, were performed on the fabricated devices and after a soft annealing treatment. The annealed cells exhibit an increased open-circuit voltage and a larger Nyquist plot radius. To interpret these results, a numerical model was developed in a TCAD environment. Simulations reveal that traps located at the p/i interface (MoOx/i-a-Si:H) significantly affect the impedance spectra, with higher trap concentrations leading to smaller Nyquist plot circumferences. The numerical impedance curves were aligned to the experimental data, enabling extraction of the interfacial traps concentration. The results highlight the sensitivity of impedance spectroscopy to interfacial quality and confirm that the performance improvement after soft annealing is primarily due to reduced defect density at the MoOx/i-a-Si:H interface. Full article

Bypass diode faults are among the most hard-to-detect but impactful anomalies in photovoltaic (PV) systems, especially under partial shading conditions, where their electrical signatures often resemble those caused by non-critical irradiance variations. This study presents a systematic simulation-based investigation into how different bypass diode fault types—short-circuited, open-circuited, and healthy—affect the electrical behavior of PV strings under diverse irradiance profiles. A high-resolution MATLAB/Simulink model is developed to simulate 27 unique diode fault configurations across multiple shading scenarios, enabling the extraction of key features from resulting I–V curves. These features include global and local maximum power point parameters, open-circuit voltage, and short-circuit current. To address the challenge of feature redundancy and classification ambiguity, a preprocessing step is applied to remove near-duplicate instances and improve model generalization. An artificial neural network (ANN) model is then trained to classify the number of faulty bypass diodes based on these features. Comparative evaluations are conducted with support vector machines and random forests. The results indicate that the ANN achieves the highest test accuracy (93.57%) and average AUC (0.9925), outperforming other classifiers in both robustness and discriminative power. These findings highlight the importance of feature-informed, data-driven approaches for fault detection in PV systems and demonstrate the feasibility of diode fault classification without precise fault localization. Full article

Solar photovoltaic (PV) power is an important force in promoting the transformation of the energy structure. An increase in PV panel temperature reduces open-circuit voltage and fill factor, thereby increasing the recombination of internal charge carriers and leading to a decrease in the output power of PV systems. When PV panels operate in the environment, high solar intensity may rapidly heat PV panels to very high temperature levels, converting a significant proportion of solar energy into waste heat. Waste heat further reduces the efficiency of PV panels. Therefore, effective cooling methods are important in improving the electrical performance and reliability of PV systems. For different types of PV panel cooling methods, many research works have been conducted. Aiming at providing a relatively valuable reference for future work on PV panel cooling methods, this paper presents a comprehensive review of existing research on cooling methods for PV panels. Relevant issues of eight types of PV panel cooling methods are introduced, including working principles, typical research, advantages, disadvantages, existing problems, and future research directions. Full article

Intermediate-band photovoltaics promise single-junction efficiencies that exceed the Shockley and Queisser limit, yet viable material platforms and device geometries remain under debate. Here, we perform comprehensive two-dimensional device-scale simulations using Silvaco Atlas TCAD to analyze p-i-n In_0.20Ga_0.80N solar cells in which the intermediate band is supplied by In_0.35Ga_0.65N quantum dots located inside the intrinsic layer. Quantum-dot diameters from 1 nm to 10 nm and areal densities up to 116 dots per period are evaluated under AM 1.5G, one-sun illumination at 300 K. The baseline pn junction achieves a simulated power-conversion efficiency of 33.9%. The incorporation of a single 1 nm quantum-dot layer dramatically increases efficiency to 48.1%, driven by a 35% enhancement in short-circuit current density while maintaining open-circuit voltage stability. Further increases in dot density continue to boost current but with diminishing benefit; the highest efficiency recorded, 49.4% at 116 dots, is only 1.4 percentage points above the 40-dot configuration. The improvements originate from two-step sub-band-gap absorption mediated by the quantum dots and from enhanced carrier collection in a widened depletion region. These results define a practical design window centred on approximately 1 nm dots and about 40 dots per period, balancing substantial efficiency gains with manageable structural complexity and providing concrete targets for epitaxial implementation. Full article

Perovskite solar cells (PSCs) leverage the exceptional photoelectric properties of perovskite materials, yet interfacial energy level mismatches limit carrier extraction efficiency. In this work, energy level alignment was exploited to reduce the charge transport barrier, which can be conducive to the transmission of photo-generated carriers and reduce the probability of electron–hole recombination. We designed a dual-transition perovskite solar cell (PSC) with the structure of FTO/TiO₂/Nb₂O₅/CH₃NH₃PbI₃/MoO₃/Spiro-OMeTAD/Au by finite element analysis methods. Compared with the pristine device (FTO/TiO₂/CH₃NH₃PbI₃/Spiro-OMeTAD/Au), the open-circuit voltage of the optimized cell increases from 0.98 V to 1.06 V. Furthermore, the design of a circular platform light-trapping structure makes up for the light loss caused by the transition at the interface. The short-circuit current density of the optimized device increases from 19.81 mA/cm² to 20.36 mA/cm², and the champion device’s power conversion efficiency (PCE) reaches 17.83%, which is an 18.47% improvement over the planar device. This model provides new insight for the optimization of perovskite devices. Full article

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs. Full article

Perovskite solar cells (PSCs) have already been reported as a promising alternative to traditional energy sources due to their excellent power conversion efficiency, affordability, and versatility, which is particularly relevant considering the growing worldwide demand for energy and increasing scarcity of natural resources. However, operational concerns under environmental stresses hinder its economic feasibility. Through the addition of cesium (Cs), this study investigates how to optimize perovskite solar cells (PSCs) based on methylammonium lead-iodide (MAPbI₃) by creating mixed-cation compositions of MA_1−xCs_xPbI₃ (x = 0, 0.25, 0.5, 0.75, 1) for devices A to E, respectively. The impact of cesium content on the following factors, such as open-circuit voltage (V_oc), short-circuit current density (J_sc), fill factor (FF), and power conversion efficiency (PCE), was investigated using simulation software, with ITO/TiO₂/MA_1−xCs_xPbI₃/Spiro-OMeTAD/Au as a device architecture. Due to diminished defect density, the device with x = 0.5 (MA_0.5Cs_0.5PbI₃) attains a maximum power conversion efficiency of 18.53%, with a V_oc of 0.9238 V, J_sc of 24.22 mA/cm², and a fill factor of 82.81%. The optimal doping density of TiO₂ is approximately 10²⁰ cm⁻³, while the optimal thicknesses of the electron transport layer (TiO₂, 10–30 nm), the hole-transport layer (Spiro-OMeTAD, about 10–20 nm), and the perovskite absorber (750 nm) were identified to maximize efficiency. The inclusion of a small amount of Cs may improve photovoltaic responses; however, at elevated concentrations (x > 0.5), power conversion efficiency (PCE) diminished due to the presence of trap states. The results show that mixed-cation perovskite solar cells can be a great commercially viable option because they strike a good balance between efficiency and performance. Full article

Flexible perovskite solar cells (FPSCs) hold great promise for lightweight and wearable photovoltaics, but improving their efficiency and durability under mechanical stress remains a key challenge. In this work, we fabricate and characterize flexible planar FPSCs on a polyethylene terephthalate (PET). A phenethylammonium iodide (PEAI) surface passivation layer is introduced on the perovskite to form a two-dimensional capping layer, and its impact on device performance and stability is systematically studied. The champion PEAI-passivated flexible device achieves a power conversion efficiency (PCE) of ~16–17%, compared to ~14% for the control device without PEAI. The improvement is primarily due to an increased open-circuit voltage and fill factor, reflecting effective surface defect passivation and improved charge carrier dynamics. Importantly, mechanical bending tests demonstrate robust flexibility: the PEAI-passivated cells retain ~85–90% of their initial efficiency after 700 bending cycles (radius ~5 mm), significantly higher than the ~70% retention of unpassivated cells. This work showcases that integrating a PEAI surface treatment with optimized electron (SnO₂) and hole (spiro-OMeTAD) transport layers (ETL and HTL) can simultaneously enhance the efficiency and mechanical durability of FPSCs. These findings pave the way for more reliable and high-performance flexible solar cells for wearable and portable energy applications. Full article

This study provides an in-depth numerical simulation to optimize the structure of InGaN-based p-i-n single homojunction solar cells using SCAPS-1D software. The cell comprised a p-type ${\mathrm{I}\mathrm{n}}_{0.6}{\mathrm{G}\mathrm{a}}_{0.4}\mathrm{N}$ layer, an intrinsic i-type ${\mathrm{I}\mathrm{n}}_{0.52}{\mathrm{G}\mathrm{a}}_{0.48}\mathrm{N}$ layer, and an n-type ${\mathrm{I}\mathrm{n}}_{0.48}{\mathrm{G}\mathrm{a}}_{0.52}\mathrm{N}$ layer. A systematic parametric optimization methodology was employed, involving a sequential investigation of doping concentrations, layer thicknesses, and indium composition to identify the optimal device configuration. Initial optimization of doping levels established optimal concentrations of ${\mathrm{N}}_{\mathrm{d}}=1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$ for the p-layer and ${\mathrm{N}}_{\mathrm{a}}=8\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$ for the n-layer. Subsequently, structural parameters were optimized through systematic variation of layer thicknesses while maintaining optimal doping concentrations. The comprehensive optimization culminated in the identification of an optimal device architecture featuring a p-type layer thickness of 0.2 μm, an intrinsic layer thickness of 0.4 μm, an n-type layer thickness of 0.06 μm, and an indium composition of x = 0.59 in the intrinsic layer. This fully optimized configuration achieved a maximum conversion efficiency (η) of 21.40%, a short-circuit current density (${\mathrm{J}}_{\mathrm{s}\mathrm{c}}$) of 28.2 mA/cm², and an open-circuit voltage ($V_{oc}$) of 0.874 V. The systematic optimization approach demonstrates the critical importance of simultaneous parameter optimization in achieving superior photovoltaic performance, with the final device configuration representing a 30.01% efficiency improvement compared to the baseline structure. These findings provide critical insights for improving the design and performance of InGaN-based solar cells, serving as a valuable reference for future experimental research. Full article